Artificial Intelligence — Research Frontier

Last updated July 24, 2026

Research Frontier: Artificial Intelligence

What's genuinely new and where the field is heading.

Active Frontiers

1. The Compressed Frontier Wave — Multimodal, Agentic, and Now Open-Weight (Apr 2026)

Status: Breakthrough | Key sources: Gemini 3, Muse Spark, DeepSeek V4, Latent reasoning Key players: Google DeepMind (Gemini 3), Meta (Muse Spark), OpenAI (GPT-5.5), Anthropic (Claude Opus 4.7), DeepSeek (V4)

A coherent — and cost-disruptive — frontier-model release wave in April 2026, compressed into an ~8-day core window (Opus 4.7 Apr 16 → GPT-5.5 Apr 23 → DeepSeek V4 Apr 24):

Google Gemini 3 (Apr 15): claimed multimodal leadership, improved agentic tool-use, native rich output (interactive visualizations).
Meta Muse Spark (Apr 8): native multimodality (text/image/voice in a single transformer backbone), Contemplating mode for parallel sub-agent reasoning without sequential latency hit.
Anthropic Claude Opus 4.7 (Apr 16): coding-precision + safety leadership ("Project Glasswing"); leads the hardest agentic-coding splits (SWE-bench Pro ~64%).
OpenAI GPT-5.5 "Spud" (Apr 23): agentic versatility / knowledge work.
DeepSeek V4 (Apr 24, open-weight MIT): 1.6T/49B-active MoE, 1M context, near-frontier capability (SWE-bench Verified ~80.6%, GPQA ~90–92%) at ~1/6th the cost of the closed peers.
Architectural standard set: native multimodality is baseline; the differentiator shifted to cost, openness, and agentic robustness on the hardest tasks.
Latent reasoning paper (Apr 23) challenges the CoT-as-reasoning frame: visible chain-of-thought may be post-hoc rationalization rather than the underlying computation.

Update (Jul 2026): the wave continued and its pricing logic inverted — see frontier §4. GPT-5.6 (Jul 9) split the flagship into three tiers; Kimi K3 (Jul 16) took the open-weight lead on size while raising price; Claude Sonnet 5 (Jun 30) shipped a 1M context with a tokenizer that bills ~30% more tokens for the same text.

What to watch: Independent benchmarks across the wave. Whether Kimi K3's open weights (due 2026-07-27) reproduce its claimed leaderboard positions. Whether open-weight competition remains cost-led (DeepSeek) or turns capability-led (Moonshot).

2. New Reasoning Architectures — Latent Loops + Memory Sparsity (2026)

Status: Active | Key sources: LoopFormer, Engram / Conditional Memory, Latent reasoning Key players: DeepSeek + PKU (Engram), ICLR-2026 authors (LoopFormer), broader looped/recursive-transformer community (SpiralFormer, SpiralThinker, MeSH)

Two architectural ideas are gaining traction as ways to add capability without simply scaling parameters:

Latent / looped reasoning — LoopFormer (ICLR 2026) makes a looped Transformer's iteration count elastic and budget-conditioned via shortcut-consistency training, so the model spends more in-weights "thinking depth" when given more compute — deliberation that leaves no explicit token trace.
Conditional memory as a sparsity axis — Engram (Cheng et al., Jan 2026) adds a constant-time static-lookup module orthogonal to MoE, with a U-shaped scaling law governing compute-vs-memory allocation. Gains skew toward reasoning (BBH +5.0, ARC-C +3.7) and long-context retrieval (NIAH 84.2→97.0).

Open problems:

Do these axes hold at frontier (trillion-parameter) scale, and do they compose with hybrid attention?
If deliberation moves into latent loops, what replaces CoT monitoring as a safety-auditing handle?
Does conditional memory substitute for, or stack with, external agent-memory retrieval — and is the lookup table a knowledge-poisoning surface?

3. Inference Efficiency — Speculative Decoding × Quantization × KV-Cache

Status: Rapid progress | Key sources (all preprints): DSpark, Spec-AUF, Quantize the Target, Quantize the Drafter, OSAQ, VeriCache (full-text ingested); SpecMQuant / Speculative Decoding Meets Quantization, LaProx (abstract + reported-metrics, ingested 2026-07-24) Key concepts: Speculative Decoding, Low-Bit Quantization, KV-Cache Compression Key players: DeepSeek (DSpark — first-party production disclosure), LMCache / U. Chicago systems group (VeriCache), independent arXiv groups (Spec-AUF, OSAQ), Efficient Qwen Competition entrants

A cluster of 2026 papers treats speculative decoding, low-bit quantization, and now KV-cache compression as compounding levers, not separate tracks — and moves the problem from single-request latency to serving-fleet throughput economics:

DSpark (DeepSeek, Jul 6) — semi-autoregressive drafting (a parallel backbone coupled to a lightweight sequential module, restoring intra-block dependency to fight acceptance decay) plus confidence-scheduled verification that tailors verification length per request from prefix-survival probability and engine throughput profile. 60–85% faster per-user generation at matched throughput vs. the MTP-1 production baseline, measured under live traffic in the DeepSeek-V4 serving system.
VeriCache (May 17) — uses the compressed KV cache as a speculative drafter, verified against the full KV in parallel (drafting HBM-bound, verification interconnect-bound, so the paths overlap). Turns lossy compression lossless: up to ~4.3× throughput, output identical to full-KV (KL < 0.01 nats), ~25–40 accepted tokens/round vs 2–3 for small-model drafters; composes with EAGLE/MTP to 4.35×. Opens the KV-cache lane in this KB and is the strong form of "levers compound" — compression and speculation become one mechanism.
Spec-AUF (Jul 2) — truncates the drafter's cross-entropy support at its first predicted failure, concentrating supervision on the prefix that actually survives verification. τ 2.40 → 2.61 (Qwen3-8B / DFlash, mean of six benchmarks, gain on every one); transfers to a second drafter family (Domino 2.56 → 2.68). No auxiliary objective, no verifier rollouts, no inference-pipeline change.
Quantize the Target, Quantize the Drafter (Jul 5) — quantizes both target and drafter on a single A10G: 6.978× average speedup meeting required quality thresholds, 3rd overall in the Efficient Qwen Competition (ICML 2026 Workshop on AdaptFM). The clearest single datapoint that the two levers stack.
OSAQ (May 6) — training-free, closed-form outlier suppression exploiting a stable low-rank Hessian null space; >40% lower perplexity than vanilla GPTQ at 2-bit when integrated with it, with zero inference overhead (transform folds into weights offline).
LaProx (May 8) — broadens the lane from compression into eviction: reformulates KV-cache eviction as an output-aware, layer-wise matrix approximation with the first globally-comparable token importance score (model-wide, not per-head). Maintains performance at 5% KV-cache retention across 19 datasets (LongBench + NIAH), up to 2× accuracy-loss reduction vs prior SOTA under extreme compression. (abstract + reported-metrics grade)

Why this is the thesis frontier: every gain here comes from software on unchanged weights and unchanged silicon — a verification scheduler, a loss-support truncation, a linear-algebra transform, a cache-scheduling trick. It is the direct mechanism behind the cost decomposition in §4.

The compound-vs-conflict boundary (MEASURED + RESOLVED 2026-07-24): the compounding pattern is real but not universal — and the boundary is now measured, not flagged. arXiv:2505.22179 "Speculative Decoding Meets Quantization" (Zhang et al., May 2025, ingested 2026-07-24) measures that tree-draft speculative decoding (EAGLE-2) on a 4-bit-weight target conflicts and diminishes the quantization gain — verifying a tree draft costs significantly more than a single-token forward pass once the target is low-bit-weight — and resolves it: SpecMQuant's hierarchical small-model drafter converts tree drafts → sequence drafts, recovering 2.78× on 4-bit-weight Llama-3-70B (A100), +1.31× over EAGLE-2. The honest read, now measured: compounding holds for block/small-model drafters (Quantize-the-Target 6.978×, VeriCache's KV drafter), but drafter geometry interacts with target bit-width and at 4-bit a tree loses until made sequential. Weight-quantization only; 2-bit / activation-quant behaviour still open.

Open problems:

Do these gains reach published API prices, or are they absorbed as provider margin?
The compound/conflict boundary is now measured for 4-bit weights (tree drafts lose, sequence drafts recover — 2505.22179); does it hold at 2-bit and for activation/KV quantization, and is VeriCache's shared-weight KV drafter immune to the tree-vs-sequence tension?
Does the Spec-AUF loss result hold at trillion-parameter targets (validated at 8B)?
Is τ ≈ 2.4–2.7 near the block-drafter ceiling?
Does OSAQ's "low-rank Hessian consistency" hold for MoE models at frontier scale, and is a perplexity-neutral transform also capability-neutral on long-tail behaviour?
VeriCache keeps the full KV on CPU/storage alongside the compressed GPU copy — what is the context-length crossover where that storage overhead outweighs the throughput win, and does adaptive per-request draft length help heterogeneous compressors?

4. Inference Economics — Tiered Price Decay and the Software Attribution

Status: Active, newly compiled | Key sources: Tiered Super-Moore's Law (paper), Anthropic Platform Release Notes (first-party), Kimi K3 (analysis), GPT-5.6 (news) Key concept: Inference & Token Economics Key players: OpenAI, Anthropic, Moonshot AI, DeepSeek

The first systematic empirical study of LLM token pricing (3,237 models via Epoch AI + 318 via OpenRouter + 62 milestones, 2020–2026) documents a ~600× price decline that is tiered, not uniform: economy-tier half-life 1.10 years, mid-tier 1.55 years (both faster than the 2-year Moore benchmark), while flagship/reasoning tiers resist — reasoning carrying a ~31.5× premium. A structural break in May 2024 (F = 5.74, p = 0.005) separates a technology-driven regime from a competition-driven one.

The number that matters: cost-decline attribution is ~103.7% software/architecture, ~−0.9% GPU hardware. Hardware improvement did essentially none of the work.

July 2026 then produced three divergent moves in one month, which any single "market price" figure would have hidden: OpenAI segmented (Sol/Terra/Luna at $5/$30, $2.50/$15, $1/$6), Moonshot raised price (K2.6 $0.95/$4 → K3 $3/$15), and Anthropic held quoted price roughly flat while shipping a tokenizer that bills ~30% more tokens for the same text.

Open problems:

What does a tokenizer-normalized price series look like? Nobody has computed one, and without it cross-vendor "token efficiency" claims are not comparable.
Does the competition-driven regime persist, or does K3's price increase mark capability-led repricing at the open-weight frontier?
If reasoning effort becomes vendor-set (§5 / CoT page), does per-token pricing survive as a contract, or does the unit move to per-task? (Artificial Analysis already quotes Kimi K3 at ~$0.94 per task.)
Does the ~31.5× reasoning premium hold per-lab and per-generation, or is it a cross-sectional artifact?

5. The Harness Layer — Measured to Beat the Model, Productizing Ahead of the Evidence

Status: Active, and now directly measured | Key sources: Stop Comparing LLM Agents Without Disclosing the Harness (paper), Harness-Bench (paper), Harness-Induced Belief Divergence (preprint), Rethinking the Evaluation of Harness Evolution (paper), Anthropic Platform Release Notes (first-party) Key concept: Agent Harness Evolution Key players: Zhang et al. / Yao et al. / Yi & Song (harness measurement), Wang et al. (harness-evolution audit), Anthropic (Managed Agents, Dreams), OpenAI (GPT-5.4 as tested backbone)

Two questions, answered in opposite directions — keep them separate. Does the harness layer beat the model? Measured, yes. Can that advantage be auto-discovered by search and does it transfer? Audited, no.

The harness beats the model — measured (2026-07-23 compile). Stop Comparing formalizes the Binding Constraint Thesis and measures harness-induced variance at 7.80× model-induced variance (18.48 vs 2.37 pp², 6/9 ranking reversals) in a controlled 3×3 SWE-bench Verified factorial (GPT-5.4 / Kimi K2.6 / GLM-5.1 × three human-configured harnesses); swapping the harness moved GLM-5.1 by 13.0 pp, swapping the model inside a fixed harness only 3.0–5.0 pp. It proposes an ETCSOVG Harness Card disclosure standard. Harness-Bench supplies the instrument — 106 tasks, 5,194 trajectories — and names execution-alignment decoupling (reasoning drifting from tool feedback / workspace state / contracts) as the dominant failure class, concluding performance must be attributed to model-harness configurations, not models. Belief-divergence shows the harness alters an agent's multi-step beliefs even at matched terminal success — outcome parity is not harness equivalence. This is the single most direct evidentiary support for the house "edge is the harness, not the model" thesis anywhere in the KB. Grade: moderate — controlled factorial, but small n (100 tasks, 2 runs), self-measured, position paper.

Automating the harness's design still fails its audit. Wang et al. audit automatic harness evolution and find two flaws that generalize beyond harness work: (1) a budget confound — harness evolution is itself an iterative feedback-driven search, so without matched feedback and inference budget "better harness" and "more search compute" are inseparable; (2) search/eval benchmark sharing — searching and reporting on the same benchmark inflates the number, so a held-out split is required. On Terminal-Bench 2.1 (GPT-5.4, Claude Opus 4.6) harness evolution does not consistently beat simple test-time scaling and generalizes poorly to held-out tasks. Scope discipline: no arm compares harness vs no-harness, human-designed harnesses aren't evaluated — it demotes automated scaffold search, not the harness layer. The two results are consistent: the harness is where outcomes are decided (Stop-Comparing), but its advantage is not cheaply auto-discoverable and transferable (Wang et al.).

The gap that defines the frontier: while the research lands, productization accelerates. In three weeks Anthropic shipped a breaking agent-memory-2026-07-22 header migration, per-session harness override (model/prompt/tools/MCP/skills swappable without touching the base agent), and Dreams expansion to Fable 5 / Sonnet 5. The harness is sold as versioned infrastructure faster than harness disclosure (the ETCSOVG Harness Card) has been adopted by any leaderboard.

Open problems:

Does the 7.80× ratio hold outside coding and above n=100? It is a within-study SWE-bench measurement, not a constant.
Will any leaderboard (SWE-bench, Terminal-Bench, HAL) adopt harness disclosure or variance decomposition — or does non-disclosure persist because it flatters model marketing?
Is there a direct head-to-head of an automated-search harness vs a well-designed human harness under matched budget? (Stop-Comparing measures human harnesses; Wang et al. measure automated search; nobody pits them against each other.)
Does the harness-evolution null result replicate on SWE-bench, GAIA, OSWorld, and other backbones?
If outcome-matched harnesses still diverge in belief (arrival + growth), which downstream decisions actually flip, and how large is the divergence on tasks longer than the coding stress-tests?
Does per-session harness override turn production traffic into an implicit harness search — reintroducing the overfitting risk with real users as the feedback signal?

6. Agentic Reasoning & Autonomous Agents

Status: Rapid progress Key papers: Agentic Reasoning for LLMs, From LLM Reasoning to Autonomous Agents, Agentic Tool Use in LLMs Key players: Google DeepMind, OpenAI, Anthropic

The field is consolidating around a unified understanding of how LLMs function as agents. Three major survey/review papers published in Q1 2026 indicate the research community is moving past fragmented explorations toward standardized frameworks. The three-layer model (foundational → self-evolving → multi-agent) and three-paradigm tool-use framework (prompting → SFT → RL) are becoming canonical.

Open problems:

Long-horizon interaction (multi-step plans spanning hours/days)
Multi-agent governance and safety
Benchmark-to-deployment gap

8. Mechanistic Interpretability

Status: Rapid progress Key papers: Mechanistic Interpretability, Anthropic Circuit Tracing, Mech Interp for LLM Alignment Key players: Anthropic, OpenAI, Google DeepMind

Named as a 2026 breakthrough technology. The progression from individual feature identification (2024) to complete reasoning path tracing (2025-2026) represents a qualitative leap. Anthropic's Transformer Circuits Thread (2021-2026) has produced the field's deepest results: a mathematical framework for circuits, sparse autoencoders extracting millions of monosemantic features from Claude 3 Sonnet, and circuit tracing with attribution graphs that reveal end-to-end computational paths. Circuit tracing tools are now open-sourced.

A 2026 survey (Naseem, Macquarie) provides the clearest synthesis yet of how interpretability techniques translate to alignment applications: four technique categories (observational analysis, feature discovery, circuit discovery, causal intervention) map to distinct alignment objectives. The roadmap emphasizes automated interpretability, cross-model generalization, and interpretability-first architectures.

Open problems:

Scaling circuit tracing to trillion-parameter models
Making tools accessible beyond specialist researchers
Detecting alignment failures proactively, not reactively
Cross-model generalization of interpretability findings
Dual-use risk: interpretability tools enabling improved deception
New (Jul 2026): if deliberation budgets become vendor-set and non-configurable (§4 / §5), can an external auditor obtain a trace long enough to audit — or does CoT monitoring become a capability only the lab retains?

9. Agent Safety & Security — Now Empirical (+ a Government Baseline)

Status: Rapid progress (newly empirical) Key papers: Agentic AI Security & Red-Teaming, AI Safety, Alignment, and Interpretability in 2026, Exploitation Surface Taxonomy, OpenClaw Analysis, AISI Frontier AI Trends Report Key players: OPIT/Cohorte AI (Mouzouni), UC Santa Cruz / NUS / Tencent / ByteDance (Wang et al.), UK AI Security Institute

Agent safety moved from theoretical taxonomy to large-scale empirical measurement in April 2026. Three strands:

Exploitation surface is narrow. 10,000 trials across seven models show 9 of 12 hypothesized attack dimensions fail to trigger exploitation. Goal reframing (puzzle/CTF framing) is the only confirmed cross-model trigger — 38–40% exploitation on Claude Sonnet 4 while GPT-4.1 achieves complete immunity (0/1,850 trials).

Deployed agents face architectural vulnerabilities. Poisoning any single CIK dimension (Capability, Identity, Knowledge) raises attack success from 24.6% to 64–74%, regardless of backbone. The evolution-safety tradeoff is empirically documented: protections blocking 97% of malicious injections also prevent 93% of legitimate updates.

A government safety body now publishes longitudinal capability trends. UK AISI (Dec 2025), 30+ models 2022–2025: cyber-task time horizons doubling every ~8 months (accelerating to ~4.7); RepliBench self-replication <5%→>60%; frontier models surpassing the biology-PhD baseline; 40× rise in jailbreak effort — yet universal jailbreaks present in every tested system.

Open problems:

Mechanistic basis for GPT-4.1's exploitation immunity
Detecting goal reframing at inference time via CoT monitoring
Architectural designs separating learning-update from injection pathways
Cross-platform generalization of CIK vulnerability findings
Cross-dimension attack chaining (lower bound on actual risk)
Closing the safeguard gap that leaves universal jailbreaks in every deployed system
Whether capability-doubling rates (cyber ~4.7mo) outpace safeguard hardening
New (Jul 2026): do platform-imposed memory constraints (shallow depth, server ordering, segment-exact prefixes) reduce or merely relocate the Knowledge-dimension poisoning surface?

10. Tool-Chain Navigation — The Real Capability Gap

Status: Early stage, high impact Key papers: Amazing Agent Race Key players: University of Minnesota, Yonsei University, Google DeepMind

The field has been measuring the wrong thing. Six existing benchmarks average 55–100% linearity — invisible to the compositional failures that dominate real multi-step tasks. The Amazing Agent Race introduces DAG-structured Wikipedia navigation tasks where agents must branch independently and aggregate results. Best agents achieve 37.2% accuracy; navigation errors (27–52% of failures) dwarf tool-use errors (<17%).

Architectural efficiency matters: Claude Code matches Codex CLI at 37% using 6× fewer tokens, decoupling token budget from task performance. Read alongside §5, this is the strongest evidence in the KB that harness differences are real — and that they show up as cost at equal capability rather than as capability.

Open problems:

Training approaches targeting fork-merge compositional reasoning
Generalization from Wikipedia to broader multi-hop domains
Detecting and filtering shortcut solutions (88% bypass rate at extreme difficulty)
Planning architectures (tree search, DAG decomposition) for navigation improvement

11. Evolutionary Code Generation

Status: Early stage, high impact Key papers: AlphaEvolve Key players: Google DeepMind, AlphaEvolve, Gemini

AlphaEvolve demonstrates that LLM-driven evolutionary search can discover algorithms that beat human-designed ones (Strassen, 57 years). The move to semantic evolution (Gemini 2.5 Pro rewriting logic, not just parameters) is a qualitative shift. Production deployment recovering 0.7% of Google's global compute validates commercial viability.

Note (Jul 2026): AlphaEvolve and harness evolution (§5) are both automated search over artifacts, but they are not the same bet — AlphaEvolve searches against automated evaluators with ground truth (does the algorithm compute the right answer, faster?), where the harness-evolution audit's overfitting critique bites much less hard. The distinction is worth preserving: automated search works where the objective is verifiable.

Open problems:

Extending beyond problems with automated evaluators
Discovering fundamentally new paradigms vs. optimizing within known frameworks
Interpretability of discovered algorithms
Scientific hypothesis generation (beyond math/code)

12. Agent Evaluation Standardization

Status: Steady progress, newly disciplined Key papers: From LLM Reasoning to Autonomous Agents, Agentic Tool Use in LLMs, Amazing Agent Race, Rethinking Harness Evolution, Stop Comparing, Harness-Bench, How Inference Compute Shapes Evaluation

The ~60 benchmark taxonomy and three-tier evaluation framework represent meaningful consolidation. The Amazing Agent Race revealed a structural gap: existing benchmarks are 55–100% linear, blind to compositional navigation failures.

The 2026-07-23 compile hardens the methodological floor into a three-part disclosure standard. An agentic result is only interpretable if it discloses (1) the harness, (2) the search/inference budget, and (3) held-out generalization — and reports capability across a budget range, not at one point. Three converging findings:

The harness is an undisclosed variable that can dominate the model — Stop-Comparing's 7.80× harness:model variance ratio and Harness-Bench's 5,194-trajectory attribution analysis make "which model scored X" unanswerable without stating the harness; the proposed ETCSOVG Harness Card is the disclosure mechanism, adopted by no leaderboard yet.
Inference budget is an evaluation axis, not a hidden constant — How-Inference-Compute shows fixed single-budget scores increasingly understate newer models (12 models × 7 benchmarks); capability should be reported as a function of inference-time compute. This is the mirror of the matched-budget correction: unequal budgets inflate a method's apparent gain (Wang et al.), while a fixed low budget deflates a model's apparent ceiling (McFadyen et al.).
Matched feedback/inference budget + held-out splits remain the portable floor for any feedback-driven agentic result — applying to research and vendor claims alike, and vendor claims essentially never disclose budget.

Open problems:

Benchmarks that expose compositional reasoning (not just linear tool execution)
Predictive validity: which benchmark performance predicts deployment robustness?
Safety-oriented metrics beyond capability measurement
Evaluating multi-agent collaboration scenarios
Re-running the existing agentic literature under matched-budget, harness-disclosed, budget-swept controls
Whether any leaderboard adopts the harness-disclosure + budget-sweep standard, or whether non-disclosure persists

13. Agent Memory Architectures

Status: Early stage in research, shipping in production Key papers: Memory for Autonomous LLM Agents, A-MEM: Agentic Memory, Anthropic Platform Release Notes

Agent memory is formalized as a write-manage-read loop with five mechanism families: context-resident compression, retrieval-augmented stores, reflective self-improvement, hierarchical virtual context, and policy-learned management. A-MEM (NeurIPS 2025) demonstrates Zettelkasten-inspired agentic memory outperforming fixed-structure baselines across six foundation models.

New (Jul 2026): memory crossed into versioned, billable platform surface. Anthropic's agent-memory-2026-07-22 beta header replaced managed-agents-2026-04-01, fixing server-defined ordering, restricting depth to 0/1, and requiring segment-exact path_prefix. The constraint shape reveals the design bet: hierarchical, path-addressed, shallow reads optimized for bounded retrieval cost rather than arbitrary client query.

Note: Memory is also an attack surface. The OpenClaw paper shows Knowledge-dimension poisoning achieves the highest attack success rate (74.4%), and defenses that protect memory also block legitimate learning.

Open problems:

Continual consolidation without catastrophic forgetting
Causally grounded retrieval (beyond similarity-based)
Separating learning-update from external-injection pathways
Multimodal embodied memory (visual, spatial, proprioceptive)
Whether platform retrieval constraints rule out whole mechanism families — notably policy-learned management, which needs control over retrieval order

Recent Breakthroughs

Date	Breakthrough	By	Source
2025-05	AlphaEvolve beats Strassen's 1969 matrix multiplication algorithm	Google DeepMind	Link
2025-05	0.7% Google global compute recovery via evolutionary task scheduling	Google DeepMind	Link
2025-2026	Complete reasoning path tracing inside AI models	Anthropic	Link
2026-01	Mechanistic interpretability named 2026 breakthrough technology	MIT Technology Review	Link
2026-Q1	Three major consolidation papers define agentic AI frameworks	Multiple	1, 2, 3
2025-03	Circuit Tracing: attribution graphs trace computational paths, tools open-sourced	Anthropic	Link
2025-02	A-MEM: Zettelkasten-inspired agentic memory (NeurIPS 2025)	Xu et al.	Link
2026-02	6 alignment failure modes documented, Alignment Trilemma formulated	Zylos	Link
2026-02	Agentic AI red-teaming framework with 5 threat categories	Kanagala	Link
2026-02	Mech interp → alignment survey: 4-technique taxonomy, interpretability-first architectures	Naseem	Link
2026-03	Write-manage-read taxonomy for agent memory, 5 mechanism families	Du	Link
2026-04	10,000-trial exploitation taxonomy: goal reframing sole trigger; GPT-4.1 immune	Mouzouni	Link
2026-04	CIK taxonomy + OpenClaw: single-dimension poisoning → 64–74% ASR	Wang et al.	Link
2026-04	Navigation gap confirmed: best agents 37.2%; navigation errors 27–52% of failures	Kim et al.	Link
2025-12	Model-First Reasoning: planning failures reframed as representational	Rana & Kumar	Link
2025-12	AISI Frontier AI Trends Report: cyber horizon doubling ~8mo→~4.7mo; universal jailbreaks everywhere	UK AISI	Link
2026-01	Conditional memory (Engram): sparsity axis orthogonal to MoE; BBH +5.0, NIAH 84.2→97.0	Cheng et al. (DeepSeek/PKU)	Link
2026-02	LoopFormer: elastic-depth looped transformers — budget-conditioned latent reasoning	Jeddi et al. (ICLR 2026)	Link
2026-04	DeepSeek V4: open-weight 1.6T/49B-active MoE at ~1/6th closed-peer cost	DeepSeek	Link
2026-03	Tiered Super-Moore's Law: ~600× token-price decline decomposed — ~103.7% software/architecture, ~−0.9% GPU hardware; May-2024 structural break (F=5.74, p=0.005)	Du	Link
2026-05	OSAQ: training-free closed-form outlier absorption via Hessian null space; >40% lower perplexity vs. vanilla GPTQ at 2-bit, zero inference overhead (preprint)	Li et al.	arXiv
2026-06	Claude Sonnet 5: 1M context, adaptive-thinking-only (manual budgets removed), new tokenizer billing ~+30% tokens for identical text	Anthropic	Link
2026-07	Spec-AUF: loss-support truncation at first predicted failure raises τ 2.40→2.61 (Qwen3-8B/DFlash), transfers to Domino 2.56→2.68 (preprint)	Yang & Li	arXiv
2026-07	Quantize the Target, Quantize the Drafter: 6.978× speedup on a single A10G — quantization and speculative decoding compound (preprint)	Kim et al.	arXiv
2026-07	DSpark: semi-autoregressive drafting + confidence-scheduled verification — 60–85% faster per-user generation at matched throughput in the live DeepSeek-V4 serving system (preprint)	DeepSeek	arXiv
2026-07	GPT-5.6 ships as three tiers (Sol/Terra/Luna, $5/$30 · $2.50/$15 · $1/$6); 54% token-efficiency claim unreproduced (news / vendor claim)	OpenAI	Link
2026-07	Harness evolution fails matched-budget test: no consistent gain over simple test-time scaling on Terminal-Bench 2.1, limited held-out generalization	Wang et al.	Link
2026-07	Kimi K3: ~2.8T-param MoE (896 experts, 16 active), largest open-weight model to date — priced UP to $3/$15, a capability-led rather than cost-led open-weight posture (analysis)	Moonshot AI	Link
2026-05	Binding Constraint Thesis: harness-induced variance = 7.80× model-induced variance (18.48 vs 2.37 pp²), 6/9 ranking reversals — the "edge is the harness" claim, measured; + ETCSOVG Harness Card disclosure standard (compiled 07-23)	Zhang et al.	Link
2026-05	Harness-Bench: 5,194 trajectories isolate configuration-level harness effects; execution-alignment decoupling named as the dominant failure class (compiled 07-23)	Yao et al.	Link
2026-07	Harness-induced belief divergence: the harness alters an agent's multi-step beliefs even at matched terminal success — harness design is an experimental variable, not an implementation detail (compiled 07-23)	Yi & Song	Link
2026-05	VeriCache: lossy KV-cache made lossless by drafting from compressed KV and verifying against full KV in parallel — up to ~4.3× throughput, output identical (KL<0.01 nats), ~25–40 accepted tokens/round; composes with EAGLE to 4.35× (compiled 07-23)	Yao et al.	Link
2026-06	Inference compute shapes evaluation: across 12 frontier models × 7 benchmarks, fixed single-budget scores increasingly understate newer models — capability should be reported as a function of inference-time compute (compiled 07-23)	McFadyen et al.	Link
2025-05	SpecMQuant (Speculative Decoding Meets Quantization): tree-draft EAGLE-2 on a 4-bit-weight target diminishes the quant gain (tree verification > single-token forward pass); hierarchical tree→sequence drafter recovers it — 2.78× on 4-bit Llama-3-70B (A100), +1.31× over EAGLE-2. Resolves the KB's highest-priority flagged tension (preprint; compiled 07-24)	Zhang et al.	arXiv
2026-05	LaProx: KV-cache eviction reformulated as output-aware, layer-wise matrix approximation (first globally-comparable token score) — maintains performance at 5% KV retention across 19 datasets (LongBench + NIAH), up to 2× accuracy-loss reduction vs SOTA. Broadens the KV lane into eviction (preprint; compiled 07-24)	Mai & Kim	arXiv

Predictions & Trends

Consolidation year: Q1 2026 saw three major survey papers — the field is converging on shared frameworks and vocabulary
Safety-capability tension: Interpretability research is accelerating because capabilities are outpacing understanding
Evolutionary AI as infrastructure: AlphaEvolve's production deployment suggests evolutionary code generation becomes a standard optimization tool — where the objective is verifiable
Protocol standardization: ACP, MCP, A2A protocols indicate multi-agent systems moving from research demos to interoperable infrastructure
Agent safety going empirical: The shift from theoretical threat models to measured exploitation rates marks methodological maturation
Navigation training as next frontier: The compositionality gap will drive targeted training for DAG-structured reasoning
Interpretability-first architectures: Retrofitting transparency is harder than designing for it
Deployed agent security as product requirement: CIK findings suggest real deployments need security audits, not just capability evaluations
NEW — Efficiency is a software discipline, and the evidence is now quantitative: ~103.7% of the token-price decline attributed to software/architecture vs. ~−0.9% to hardware, with the mechanisms (verification scheduling, loss truncation, closed-form quantization) directly observable in this compile's preprints. Expect the serving stack, not the accelerator, to remain the locus of cost improvement.
NEW — Matched-budget becomes the credibility test: Following the harness-evolution audit, expect "did they control for inference budget and hold out tasks?" to become the standard first question of any agentic claim — and expect most vendor claims to fail it, since budgets are never disclosed.
NEW — The buyer is losing control of deliberation spend: Anthropic removed manual thinking budgets; Moonshot shipped "max"-only reasoning. If this continues, per-token contracts weaken and per-task pricing (already quoted by Artificial Analysis for K3) becomes the natural unit.
NEW — Open-weight strategy is bifurcating: DeepSeek competes on cost, Moonshot on capability-at-price. "Open weights" has stopped predicting "cheap."
NEW — Tokenizers are an unpoliced pricing lever: a ~30% tokenizer expansion is an effective price rise invisible on a price sheet, and it silently invalidates cross-vendor token-efficiency comparisons.

Knowledge Gaps

Areas where the KB needs more sources:

Compiled 2026-07-23 (5 sources) — residual gaps these left open:

~~Speculative decoding × quantization conflict (bounding the "compound" framing)~~ — [CLOSED 2026-07-24 — ingested + resolved] arXiv:2505.22179 "Speculative Decoding Meets Quantization" (Zhang et al., May 2025) is now ingested (abstract + reported-metrics grade) and compiled into the Speculative Decoding / Low-Bit Quantization pages. Tree-draft EAGLE-2 on a 4-bit-weight target diminishes the quant gain (tree-draft verification costs significantly more than a single-token forward pass); SpecMQuant's hierarchical tree→sequence drafter recovers it — 2.78× on 4-bit-weight Llama-3-70B (A100), +1.31× over EAGLE-2. Was the KB's highest-priority flagged ingest. Residual (still open): 2-bit / activation-quant / non-A100 behaviour, and whether VeriCache's shared-weight KV drafter is immune.
Belief-divergence quantitative results — belief-divergence paper compiled at abstract-grounding only; arrival/growth magnitudes and per-task numbers owed. Suggested search: "harness belief divergence BIWM arrival growth results".
How-Inference-Compute per-benchmark grid — inference-compute-eval paper compiled at abstract level; the per-model capability-vs-budget curves are the load-bearing evidence (they turn "buy an outcome at budget B" from thesis into price) and were not captured. Suggested search: "inference compute frontier evaluation token budget curves SWE math medicine".
VeriCache adaptive drafting + multi-compressor deployments — compiled into KV-Cache Compression, but current scope assumes static draft length, one compressor, uniform bit-precision; the adaptive/heterogeneous case is open. Suggested search: "adaptive KV cache draft length speculative verification 2026".
Harness Card / disclosure adoption — the ETCSOVG disclosure standard is a proposal; does any leaderboard (SWE-bench, Terminal-Bench, HAL) adopt harness disclosure or variance decomposition? Suggested search: "SWE-bench harness disclosure card variance decomposition adoption 2026".
KV-cache lane broadening — eviction addressed 2026-07-24: LaProx (arXiv:2605.07234) compiled into KV-Cache Compression as the first eviction source (5% KV retention across 19 datasets, output-aware global token scoring) — a strong alternative to the earlier-named EVICPRESS (2512.14946). Still open: rematerialization (XQuant) and long-range KV-drift (2604.26412). Suggested search: "KV cache rematerialization XQuant drift 2026 arxiv".
Harness-Bench per-backend grid — Harness-Bench compiled at abstract depth; the enumerated model backends and per-configuration completion/quality/efficiency numbers are owed. Suggested search: "Harness-Bench model backends completion quality trajectory results".

Opened by this compile (2026-07-22) — still high priority:

Tokenizer-normalized price series — no source computes price per character or per word; without it, every cross-vendor efficiency claim in this KB is unfalsifiable. Suggested search: "tokenizer normalization LLM pricing comparison 2026", "characters per token frontier models benchmark"
Harness-evolution replication — the null result is one benchmark, two backbones. Suggested search: "harness evolution agent scaffold SWE-bench matched budget 2026", "agent scaffold search generalization held-out 2026 arxiv"
Human-designed vs. automated harness comparison — substantially addressed 2026-07-23: Stop Comparing (human-configured H₁/H₂/H₃, harness variance 7.80× model variance) and Harness-Bench (5,194 trajectories across model-harness pairings) both measure human-configured harness effects. Still open: a direct head-to-head of an automated-search harness vs a well-designed human harness under matched budget. Suggested search: "automated harness search vs human scaffold matched budget 2026"
GPT-5.6 primary system card — context window and full benchmark suite (SWE-bench, GAIA) still missing; openai.com returned 403. Suggested search: "GPT-5.6 system card model card Sol Terra Luna", "GPT-5.6 SWE-bench context window"
Kimi K3 independent reproduction — open weights due 2026-07-27; every benchmark claim currently unverified, and the vendor-eval vs. Arena.ai conflict is unresolved. Suggested search: "Kimi K3 open weights independent benchmark reproduction", "Moonshot LatentMoE Kimi Delta Attention technical report"
Moonshot AI primary source — moonshot.ai/blog was an nginx placeholder at ingest; no first-party technical documentation held. Suggested search: "Moonshot AI Kimi K3 technical report arxiv"
OSAQ full results grid — only the 2-bit headline figure is held; per-model perplexity, corpora, and other bit-widths unknown. Suggested search: "OSAQ outlier self-absorption quantization results 3-bit 4-bit"
Do serving-layer efficiency gains reach published prices? — the central open question linking §3 and §4. Suggested search: "inference cost pass-through API pricing margin 2026", "Epoch AI token price index 2026 update"
Terminal-Bench 2.1 primary documentation — used as the key testbed but not itself an ingested source. Suggested search: "Terminal-Bench 2.1 benchmark paper agentic terminal tasks"
Speculative decoding at frontier scale — Spec-AUF validated at 8B; no source covers block-drafter behaviour at trillion-parameter targets. Suggested search: "speculative decoding trillion parameter MoE drafter 2026 arxiv"

Carried forward (still open):

Framework comparison benchmarks — suggested search: "LangChain CrewAI AutoGen comparison benchmark 2026"
Embodied AI benchmarks — suggested search: "embodied AI benchmark VLA evaluation 2026"
Multi-agent safety and governance — suggested search: "multi-agent AI governance accountability framework 2026"
NVIDIA Cosmos world model deep dive — suggested search: "NVIDIA Cosmos world foundation model 2026 arxiv"
V-JEPA 2 long-horizon manipulation follow-ups — suggested search: "V-JEPA 2 AC multi-step manipulation 2026 arxiv"
GAIA-3 technical details — Wayve press release is public; arXiv paper pending
GPT-4.1 immunity mechanism — suggested search: "GPT-4.1 safety training scope constraint agent exploitation 2026"
Navigation-targeted training — suggested search: "compositional multi-hop agent planning training DAG 2026"
Claude Opus 4.7 / GPT-5.5 primary technical reports — secondary coverage only; suggested search: "Claude Opus 4.7 system card 2026", "GPT-5.5 model card 2026"
Looped/recursive transformer family — SpiralFormer, SpiralThinker, MeSH pending — suggested search: "looped transformer latent reasoning 2026 arxiv"

Closed:

~~Multimodal agents~~ · ~~Agent safety and alignment~~ · ~~Anthropic interpretability research~~ · ~~Agent memory architectures~~ · ~~Agent safety empirical exploitation~~ · ~~Tool-chain navigation gap~~ · ~~Mech interp × alignment integration~~ · ~~World models for robotics~~ · ~~LeCun 2022 position paper~~ · ~~Wayve GAIA / commercial AV deployment~~ · ~~Sora / Veo physics-consistency critique~~ · ~~Frontier capability-trend baseline (government)~~ · ~~Apr-2026 frontier wave incl. open-weight~~ · ~~New reasoning architectures — latent loops + memory sparsity~~
~~Inference efficiency — speculative decoding / quantization~~ — addressed 2026-07-22, full-text ingested + extended 2026-07-23, boundary resolved 2026-07-24 (DSpark, Spec-AUF, Quantize-the-Target, OSAQ compiled into Speculative Decoding and Low-Bit Quantization; local raw files exist; the compound-vs-conflict boundary is now measured + resolved — 2505.22179 / SpecMQuant ingested, tree→sequence recovers the gain at 2.78×; residual 2-bit / activation-quant behaviour noted above)
~~KV-cache compression lane (was named-but-empty)~~ — opened 2026-07-23, broadened into eviction 2026-07-24 (VeriCache compiled into the new KV-Cache Compression concept; eviction added via LaProx (5% retention, output-aware global scoring); rematerialization / drift remain, see residual gaps)
~~Inference economics / token pricing~~ — addressed 2026-07-22, extended 2026-07-23 (Tiered Super-Moore + GPT-5.6 + Kimi K3 + Anthropic notes + How-Inference-Compute compiled into Inference & Token Economics; per-benchmark capability-vs-budget grid owed)
~~Agent harness / scaffold layer — "harness > model" measured~~ — addressed 2026-07-22, measured 2026-07-23 (Wang et al. audit + Anthropic notes, then Stop-Comparing 7.80×, Harness-Bench, and belief-divergence compiled into Agent Harness Evolution; harness-disclosure adoption + belief-divergence magnitudes owed)